Methods and systems for an autonomous robotic platform

ABSTRACT

A system and method can provide a command and control paradigm for integrating robotic assets into human teams. By integrating sensor to detect human interaction, movement, physiology, and location, a net-centric system can permit command of a robotic platform without an OCU. By eliminating the OCU and maintaining the advantages of a robotic platform, a robot can be used in the place of a human without fatigue, being immune to physiological effects, capable of non-humanoid tactics, a longer potential of hours per day on-station, capable of rapid and structured information transfer, has a personality-free response, can operate in contaminated areas, and is line-replaceable with identical responses. A system for controlling a robotic platform can comprise at least one perceiver for collecting information from a human or the environment; a reasoner for processing the information from the at least one perceiver and providing a directive; and at least one behavior for executing the directive of the reasoner.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention relates to the command and control of roboticplatforms.

2. Description of the Related Art

Conventional approaches to command and control (“C2”) of mobile roboticplatforms, including unmanned ground, sea, or air vehicles, typicallyrequire constant human interaction or intervention. Generally, thecurrent state of robotic C2 relies on either remote control,teleoperation, or map-based semi-autonomy.

Remote control is conventionally implemented by having a remote operatordirectly control the robot. Typically, any and all actions executed bythe robot are directly controlled by the operator, who is assumed to bein line-of-sight to the robot. The operator watches the robot andcontrols it through an operator control unit (“OCU”). The OCU is aremote device that can be tethered to the remote platform, but typicallyis not. The OCU typically has a joystick or other steering controller tocontrol the movement and/or operation of the remote platform. The humanoperator must visually follow the unmanned vehicle to determine the nextcourse of action and command the unmanned vehicle through the OCU toconduct that course of action. This operation is similar to operation ofa remote-control toy car, where operation can be subject to visibilityand distance limitations.

In conventional teleoperation of a robotic platform, the OCU typicallyincludes a video display and joystick for a human operator to controlthe robotic platform. Teleoperation is similar to remote control, butthe line-of-sight restriction can be removed by utilizing sensors suchas cameras (e.g., a camera on the vehicle viewed through a video displayin the OCU) that give the operator a sense of the robot's environmentand actions. An operator watches sensor output from the robot andcontrols the robot's actions with a joystick. In one example, the OCUcan have a video display to monitor the actions of the robotic platformand/or the environment. The human operator uses the joystick on the OCUto operate the robot, making observations through the video display.

In conventional semi-autonomous control of a robotic platform, the robotfollows a sequence of GPS waypoints using sensors on board the roboticplatform to detect and avoid any obstacles it may encounter. Using aconventional map-based OCU, robots are controlled by entering sequencesof waypoints and tasks through the OCU. The robot then moves through thewaypoints, carries out the tasks autonomously, and requires retaskingupon completion or upon encountering circumstances that prohibitcompletion. For example, a human operator, based on location and limitedinformation regarding the surroundings, designates waypoints on a map oroverhead imagery, thereby commanding the robot to travel from a firstcoordinate to a second coordinate and so on to successive waypoints. Therobot can be commanded to perform designated tasks at each waypoint, oralong each path between waypoints.

SUMMARY OF THE INVENTION Summary of the Problem

There are a number of problems associated with conventional operatorcontrol of robotic platforms. Remote control of a robot is typicallylow-cost, but can only be operated in line-of-sight, and full-timeoperator attention is required. Additionally, although entities can betracked with sensors and viewed by a human operator, this conventionalmethod fails when an entity goes around a corner and cannot be tracked.Similarly, teleoperation of a robotic platform is also low-cost andfull-time operator attention is also required, although teleoperation isnot limited to line-of-sight control. The conventional, semi-autonomousmap-based system is slow, requires training, is difficult to use tore-plan, and requires a sophisticated OCU, which is often heavy andcumbersome. Additionally, if there is an unforeseen event orcircumstance, such as an obstacle or other situation that cannot behandled by its on-board programming, the robotic platform may requirehuman intervention. These conventional systems can require significantand overt human direction of robot actions. As a result, these methodscan break down when human operators are stressed or otherwisedistracted.

To make robots effective in supporting human teams, the human operatormust not only visualize the location of the unmanned vehicle, but alsounderstand the surrounding circumstances or environment. For example,referring to FIGS. 1 a and 1 b, two aerial views of squads in urbansituations are illustrated showing soldier locations and the location ofan unmanned vehicle. As shown in this example, it can be difficult for aremote operator to determine if the squad is in danger and the locationof the threat. If the operator of the unmanned vehicle can only observethe squad via the aerial view of locations illustrated in FIG. 1 b, theremote operator may not be able to discern whether the squad is taking abreak or taking cover from enemy fire. Without understanding thesituation, the remote operator may be unable to command the robotappropriately.

If the operator is local, the stress of the situation can make itdifficult for the operator to command the robot. For example, as shownin FIG. 1 a, a squad of soldiers 100 can be moving down a street in anurban area with an unmanned vehicle carrying spare ammunition andsupplies. In order to control the unmanned vehicle, a human operatorwith the squad or a remote operator through limited visibility mustexplicitly task the vehicle 120. In the instance a sniper takes a shotat the squad or an IED explodes near the squad, the soldiers 100 maytake cover behind a building or structure 110, as shown in FIG. 1 b. Theunmanned vehicle 120 does not react appropriately and follow thesoldiers because the human operator, who is concerned with his or herown life, takes cover rather than using an OCU to command the unmannedvehicle 120 to follow. As a result, the unmanned vehicle 120 maycontinue to follow its original route and traverse the street away fromthe squad.

The use of unmanned vehicles or other robotic platforms in militaryoperations can extend a team's area of influence, broaden itssituational awareness and understanding, and increase its lethality andsurvivability, while reducing the physical and cognitive burden onindividual team members. However, current unmanned vehicles can requirenear-constant human supervision and are difficult to retask when eventschange. As a result, unmanned vehicles are typically been relegated tooperations that can be done slowly and deliberately, such as explosiveordinance disposal.

Adding a second unmanned vehicle to a team can require additionalequipment, and require a second team member to operate the OCU for thatunmanned vehicle. As a result, the team can have one less soldier,rescue worker, or other type of team member for accomplishing a goal.

Controlling an unmanned vehicle through an OCU can be cognitivelydemanding. In fact, many potential military applications for robots areconsidered unworkable because of the OCU requirement. As a result,unmanned vehicles may be excluded from high-intensity situations,including those in which the unmanned vehicles can be the most useful tothe team.

Summary of the Solution

One solution to these problems can be to enable asymmetric cognitiveteams (“ACT”). For example, an ACT can be created by augmenting a mobilerobot's sensors with instrumentation of other members of the team, andusing this information in a cognitive model to enable the robot tounderstand the immediate situation and select appropriate behaviors. Arobot so equipped would be able to “do the right thing” automatically,thereby eliminating the need for cumbersome OCUs; the robot literallyacts like a member of the team, automatically adapting its actions tocomplement those of the other team members. The solution can reduce thecognitive burden on an operator by providing natural (i.e., human-like)interaction. In the example shown in FIGS. 1 a and 1 b, if the squad ofsoldiers move to a wall, an ACT-enabled robot can utilize theinformation about the change in formation along with data such as heartrate, blood pressure, and weapon status to determine whether thesoldiers are in a combat situation or are taking a break. The robot canthen automatically take the appropriate action, such as providing coverin the case of a combat situation or offering resupply if the team istaking a break. The system can enable robotic entities or unmannedvehicles to operate as effective team members without the need forconstant human direction. As a result, each human team member can actaccording to his/her training, rather than requiring a team member touse an OCU to control a robot.

The exemplary embodiments described herein can provide a command andcontrol paradigm for integrating robotic assets into human teams. Byintegrating sensors to detect capable of non-humanoid tactics, apotential of about 24 hours per day on-station, capable of rapid andstructured information transfer, has a personality-free response, canoperate in contaminated areas, and is line-replaceable with identicalresponses.

In one embodiment, a system for controlling a robotic platform comprisesat least one instrumented external entity, at least one sensor on eachteam member, one perceiver for collecting information from the at leastone instrumented external entity; a reasoner for processing theinformation from the at least one perceiver and providing a directive;and at least one behavior for executing the directive of the reasoner.

In another embodiment, a method for controlling a robotic platformcomprises the steps of developing tactical behaviors; determining amission, situation, disposition, and/or human cognitive or emotionalstate; driving a cognitive model; inferring current state, goals, andintentions; and selecting an appropriate behavior.

In yet another embodiment, a system for controlling a robotic platformcomprises a team sensor system; a software system comprising aperception component for providing information from the sensors; acognition component for estimate an intent from that information; aplaybook action generator component for determining a course of action;a playbook executor component for executing the course of actioncomplementary to the estimated intent; and an unmanned vehicleinterface.

In still yet another embodiment, a system for controlling a roboticplatform comprises at least one sensor that detects a status; a softwarecomponent that receives the status from the sensor; and the softwarecomponent comprising a cognitive model; wherein the cognitive modeldirects the robot to perform an action.

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be apparent from thedescription, or may be learned by practice of the invention. Theobjectives and other advantages of the invention will be realized andattained by the structure particularly pointed out in the writtendescription and claims hereof as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE FIGURES

The present invention will be more clearly understood from a reading ofthe following description in conjunction with the accompanying exemplaryfigures wherein:

FIGS. 1 a and 1 b show an aerial view of the location of the members ina squad;

FIG. 2 shows states of a human and a robot according to an embodiment ofthe present invention;

FIG. 3 shows a communication network between a team and a robotaccording to an embodiment of the present invention;

FIG. 4 shows a system architecture according to an embodiment of thepresent invention;

FIG. 5 shows a system architecture according to an embodiment of thepresent invention;

FIG. 6 shows a system architecture according to an embodiment of thepresent invention;

FIG. 7 shows components of a cooperative robotic weapon control systemaccording to an embodiment of the present invention;

FIG. 8 shows a system architecture according to an embodiment of thepresent invention;

FIG. 9 shows an alternative system architecture according to anembodiment of the present invention;

FIG. 10 shows a method of autonomous control according to an embodimentof the present invention;

FIGS. 11 a and 11 b show a playbook and plays for a course of action inthe playbook according to an embodiment of the present invention;

FIGS. 14 a to 14 g show a team's ingress on a location according to anembodiment of the present invention; and

FIGS. 15 a to 15 f show a team's positioning when a soldier is woundedaccording to an embodiment of the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings.

The systems and methods described herein can enable robotic platforms toperform appropriate behavior without overt human control. Control ofrobot platforms occurs without bulky and expensive OCUs. Robots canlearn tactics and appropriate behavior from humans. Robots can continueto operate when all humans are distracted or cognitively overloaded.Robots can learn from training (much like humans do), and robots canfully interact with and support human teams. As compared to conventionalsystems that typically control an unmanned or robotic vehicle through anOCU, the systems and methods described herein can observe a human and/orteam behavior and decide appropriate actions without direct tasking fromhumans. By understanding an individual's or a team's reaction to asituation, rather than understanding the entire environment andsituation, the system can command the robot accordingly. Information canbe provided about the human or the team by using sensors. A cognitivemodel on the unmanned vehicles can process the information, determinethe appropriate action based on models of human activities, and thenexecute the action and monitor the result. In one exemplary action, theunmanned vehicle can move with the team as the team moves. If the teamhalts, the unmanned vehicles know to halt. If the team assumes atactical formation, the unmanned vehicles know to move to and maintainan appropriate position in the formation.

The architecture of the system uses hardware and software, as well asinformation flow to produce an approach to robotic command and control.The systems and methods can utilize net-centric information flow and anembedded cognitive model to build a working model of the currentsituation and a human's or team's intent. The humans and roboticplatforms can interact through a communications network. Net-centricresources include sensors or instrumentation of humans and teams. Therobotic platform then generates a short-term course of action for therobotic platform to pursue, without overt human control or direction byOCUs. Eliminating an OCU and/or direct control (e.g., RF control) canenable robots to be useful without physically or cognitively burdeninghuman team members. This approach can also reduce training required forrobotic platform control and can enable learning of tactics by therobotic platform. As a result, the robotic platforms can become usefulmembers of human teams, while reducing the attention and effort on thepart of the humans to direct the robotic platforms. One potentialadvantage can be to enable robotic platforms to be useful when humansare under stress or otherwise distracted.

Referring to FIG. 2, coordination between a human and a robot can assistin replacing the burden of explicit control by a human. Coordination canbe possible through mutual monitoring and mutual understanding. Thesystem builds a cognitive state model of the human and uses it todetermine an appropriate course of action. The human transitions betweencognitive states, which can include, but are not limited to, at rest200, relaxed 210, excited 220, and frightened 230. The system can makeinferences based on known or detectable information, such as themission, situational awareness and understanding, squad dynamics,posture, physiology, and logistics state. These inferences trigger atransition in the robot's behavioral state, which can include, but arenot limited to, stopped 240, unguarded motion 250, protect 260, hide270, and guarded motion 280.

The systems and methods described herein can integrate and controlmultiple unmanned platforms (i.e., mobile robots or vehicles) into humanteams. The unmanned platforms can be interchangeably referred to hereinas a robot, robotic platform, or unmanned vehicle. The systems andmethods can be applied to any ACT-enabled platform including but notlimited to ground, air, water surface, and underwater robotic platforms,which are also referred to herein as unmanned vehicles, includingunmanned ground vehicles, unmanned aerial vehicles, unmanned surfacevehicles, unmanned subsurface vehicles, or any other type of unmannedvehicle known in the art. The unmanned vehicle is not intended to belimited to only those vehicles that cannot be manned and includes allground, air, sea, and undersea vehicles that can operate in an unmannedmode. Additionally, the unmanned mode of the unmanned vehicle is notintended to be limited to only when a human is not present on, in, ornear the vehicle.

The systems and methods can be domain-independent. For example, therobotic platform can be a supply carrier for a dismounted warfighter. Inanother example, the robotic platform can be a tank ammunition carrierthat can move into position to provide ammunition. In yet anotherexample, the robotic platform can be a surveillance air vehicle dronethat stays in position over the battlefield to give situationalawareness. The platform can also be a sensor platform and/or a weaponsand/or a logistics platform.

Although a soldier or a team member is described herein as a human, asoldier or team member can also include non-human members such astrained animals, robotic platforms, or other vehicles and equipment. Incertain configurations, the human being is an individual soldier. Inother configurations, the human is a member of a team. The soldier orteam member is not intended to be limited to only those configurationswhere a human acts with other human beings. Additionally, the soldier orteam member is not intended to be limited to a human conducting amilitary operation.

A team or squad is not intended to be limited to a plurality of humansconducting a military operation. A team can include at least two teammembers, equipment, vehicles, or other entities that function togetherfor a common purpose.

The systems and methods can be used in any applications including, butnot limited to, where tightly integrated, real-time cooperation betweenhumans and robots can be required, such as warfare (e.g., support fordismounted infantry or mounted operations), control of non-combatants(e.g., crowd control, peacekeeping patrols), and high-intensity,time-critical operations (e.g., search and rescue after man-made ornatural disasters). Additional opportunities for the systems and methodscan include tactical military applications, such as a robotic point man,robotic logistics platform, force protection sentries, and similarapplications. Other applications can include any application in which arobot can complement or augment human capabilities, such as aconstruction assistant. Additional opportunities can include sitesecurity and patrolling, human/robot First Responder teams, and anyother situation where humans can be augmented by mobile robots.Additional military applications can include vehicles used in ground andsea convoys and swarming type attack vehicles (e.g., small boats, ATVs,and Navy Seal activity). Non-military applications can include groundand air vehicles for border patrols and harbor/facility/criticalinfrastructure patrol vehicles.

As shown in FIG. 3, a team of soldiers 300 in an environment 310 cancommunicate with each other and a robot 330 through a wireless network320. Such communications can include position, pose, motion,physiological parameters, weapon status, and orientation command anddirectives. Additionally, robot sensors 360 can detect the environment310 and the team 300 and report to the robot 330 with informationincluding but not limited to a terrain map, target identification andtracking, range-finding, and designator detection. The robot 330 cancontrol the robot sensors 360 and the robot weapons 340. The status,behavior, and alerts of the robot 330 can be communicated to the team300. The robot 330 can compute formation, tactics, techniques, andprocedures, as well as soldier or team cognitive state and goals.

Improving human-robot coordination can be useful in replacing the burdenof explicit control, such as through an OCU. Coordination can bepossible through mutual monitoring and mutual understanding. The systemsand methods herein can build a cognitive state model of the team memberor team and can use it to determine the appropriate course of action.The system can use team location, physiology, and weapons status toestimate individual or team cognitive state and goals. That estimate canthen be used with triggering events to transition the robotic statemodel. The robot then can execute tactically appropriate behaviors inresponse to the state of the individual or team. For example, the robothalts when the individual or team is relaxed. As another example, whenthe individual or team is frightened, the robot can maneuver between theteam and an inferred threat.

As compared to conventional systems, the systems and methods describedherein can focus on situationally appropriate behaviors, rather thanrote execution of predefined plans. For example, the system can useinformation from the humans in the form of location, looking andpointing directions, physiological data, and other information sourcesthat may be available (e.g., machine readable high-level plans andsituational information). This information is fed to a cognitive modelthat develops a model of each human in the team as well as the team as awhole in order to estimate the actions and intent of the humans. Oncethe robotic platform can estimate the intent, the system can then usethat estimate of intent to define its own course of action. Forinstance, if a team of humans is moving through a town in a lineformation, the robot will automatically get in line (either at the endor filling a gap) and move with the team—starting, stopping and changingspeed with the team.

Generally, the systems and methods can operate as follows. Referring toFIG. 4, a cognitive model 420 has at least one perceiver 425, 430, 435,440 and a reasoner 445. The perceivers can include, but are not limitedto, a soldier perceiver 425, weapon perceiver 430, team perceiver 435,and sensor perceiver 440. The perceivers 425, 430, 435, 440 can be asoftware and/or a hardware component configured to produce tacticalinformation with confidence metrics that drives the cognitive model 420to estimate the current tactical state and condition of the team. Forexample, each perceiver 425, 430, 435, 440 can answer tactical questionsabout the current situation, such as: are the team members excited, aretheir weapons safe, are the majority of weapons pointed in the samedirection, is anyone in a tactical pose? Information from soldiers 400,405, 410, 415 can be communicated to the soldier perceiver 425, theweapons perceiver 430, and the team perceiver 435. The perceivers cancharacterize the team member's state, such as location, posture,physiology, weapon status having the safety on/off, weapon pointing, andtrigger pulls. The soldier perceiver 425 queries, for example, whetherthe soldiers are on alert or whether the soldiers are running or hiding.The weapon perceiver 430 queries, for example, whether the safety is inan “off” position, the weapon is low on ammunition, or if the weapon ispointing at a threat. The team perceiver 435 queries, for example,whether the team is in a formation and which formation, whether theformation is spreading or shrinking, whether the formation is changing,or where should the robot be in the formation. A sensor on a human or arobotic platform can be included in the perceiver or, alternatively, thesensor can be a separate component that provides information to theperceiver. The perceivers combine messages and sensor information fromthe sensors through the wireless communication system to the reasoner445 to answer tactical questions.

The reasoner 445 can consider trigger events, confidences, andsoldier/squad state to determine appropriate action. The reasoner 445can be a software and/or a hardware component configured to query theperceivers 400, 405, 410, 415 and uses the information to determine thesituation. For example, the reasoner 445 can query the perceivers 400,405, 410, 415 based on trigger events and state matching, including, forexample, increase in heart rate and blood pressure, safeties in an “off”position, trigger pulls, or postures changing to cover postures.Alternatively, the reasoner 445 can respond to a verbal override. Basedon the results, the reasoner 445 constructs a tactical picture.

A plurality of behaviors can be used to execute the reasoner's 445directives to perform tactical actions. Behaviors include, but are notlimited to, rally 450 (e.g., move to a coordinate), patrol 455 (e.g.,move in a pattern to follow a soldier or other robot), halt 460 (e.g.,hold position), and weapon 465 (e.g., aiming and firing of a weapon).Examples of rally behaviors include the robot rally on a soldier, rallyto a named waypoint, or rally to a designated position. Examples ofpatrol behaviors include point, follow soldier, formation move, patrol,and guard. Examples of weapon behaviors include cover fire, suppressionfire, sector-free fire, and IFF protection. IFF is identification friendor foe and is a procedure to identify friendly entities. Behaviors arecommunicated to a reactive obstacle avoidance system 470, whichidentifies any obstacles and commands the unmanned vehicle to go aroundthem, and a weapon control 465, which communicates with a vehiclecontrol unit 475.

Generally, the system architecture can use on-board sensors to validateand localize information received from the team and to captureinformation that is not available net-centrically. Periodically or inreal-time, the system can be provided with each team member's location,weapon state, and physiological state. In some configurations, thesystem can utilize the team member's reaction to events, which can beeasier to understand than the events themselves. The cognition componentfuses all incoming information into a tactical picture and develops anestimate of squad intent. The cognitive model consumes the perceptionestimate of higher-order team member behavior, which are observablestates that indicate the internal state of the team member. Thistactical state estimate enables the generation of an action in view ofthe current intent and short-term goals of the team members.

With regards to the system architecture, referring to FIGS. 5 and 6, asystem has C2 software 500, 600 a team sensor system (“TSS”) 505, 605and an unmanned vehicle interface or integration kit 510, 610. Thesystem uses a cognitive framework, fuses perception and net-centricinformation from the TSS 505, 605 and other sources into a cohesiveestimate of squad intent, generates a short-term robotic plan, then usestactical behaviors to execute the plan.

The C2 software 500, 600 provides functionality for perception,cognition, playbook action selection, executing and monitoring, and aninterface to the unmanned platform. The C2 software 500 can support atleast one unmanned vehicle that can, for example, maneuver with a team,execute behavioral roles, carry supplies, or resupply team members. TheC2 software 500 can also enable unmanned vehicles to support coordinatedteam tactical maneuvering, behavioral roles for sensor coverage, andprotection for weapon coverage. The C2 software 500 can provide networkinformation management and dissemination functionality to enableefficient communications between the TSS 505 and the unmanned vehicles.

The C2 software 500, 600 includes perception software 515, 615,cognition software 520, 620, playbook action-generation software 525,625, playbook action (or plan) execution 530, 630, and playbook actionfeedback 535, 636. Perception software 515, 615 can providefunctionality to sense and analyze the environment for navigational andtactical purposes, such as obstacle detection, local terrain mapping,and tactical perception and symbolization (i.e., representation of theperceived entities in terms useful to the cognitive processing andaction generation functions). The perception software 515, 615 usesnet-centric information, as well as information from sensors. Perceivers611, such as those described above with respect to FIG. 4, can operatewith a tactical module 612, which can navigate 613 by using obstacledetection 614 and a terrain map 616.

Cognitive model software 520, 620 can provide the functionality toidentify the current tactical state of team members, the team's tacticalstate as a whole, and the ability to share information between multiplecognitive models. The cognitive model 520, 620 can interact with otherparts of the system to direct sensing and perceiving resources to helpdisambiguate the existing tactical situation. The cognitive model 520,620 learns at different levels based on feedback from team members andchunking, or other short-term memory, to support human-based cues thatidentify when the system should learn a new situation. Cognitivesoftware 520, 620 can identify the state/role of a team member 621 or ateam 622 using semantic pattern recognition 623, which obtainsinformation from the perceptual synthesis 617 of the perceivers 611.Semantic pattern recognition 623 can use patterns in memory to recognizean environment and can look for further patterns or clues to furtherdistinguish the type of environment.

Robotic playbook action-generation software 525, 625 can provide thefunctionality to generate robotic actions based on the perceived stateof the team. The playbook action-generator 525, 625 can use the outputof cognitive models to process tactical state information, draw uponstored databases of tactics, techniques, and procedures (“TTP”) 624,training materials 626, and act to identify appropriate tactical actionsfor unmanned vehicles. Along with training 626 and TTPs 624, theplaybook generator 627 can use current plans 628, joint plans 629, andinformation from the navigation module 613 of the perception software515, 615. This information, along with the tactical state discerned fromthe semantic pattern recognition 623, can be provided to a playevaluator 632. The system uses spatiotemporal reasoning 631 tounderstand a situation (e.g., a formation) in a time and space analysisand tries to figure out what the team members are doing and why.Spatiotemporal reasoning 631 submits tactical queries to the perceivers611.

Playbook action execution software 530, 630 and feedback software 535,635 can provide the functionality to control and sequence the executionof short-term robotic actions, monitor their execution, identify actionfailures, and identify when squads have abandoned, replaced, or modifiedbehaviors. The play evaluator communicates with a plan or playbookaction executor 633, which also receives information from a play module634, to communicate with both a play monitor 636 and a plurality ofbehaviors 637 in the tactical behaviors module 638. The feedback manager535, 635 evaluates plan feedback in a relevance monitor 639 andcommunication monitor 641.

The C2 software 500, 600 uses TSS data and other information to estimatethe squad intent and generate a plan. The TSS 505 can includenon-intrusive sensor devices that provide information to the C2 software500 and allows for feedback to the user. A TSS Feedback Manager 540 canmanage a team member state 545, weapon state 550, state information andverbal command interface 555, and alert devices 560. The TSS feedbackmanager 540 can control the output (i.e., can decide to sendinformation) from the sensors to the network when there is a change in asensor's status. The TSS feedback manager 540 can monitor the status ofthe sensors and send information when there is a change that it deems tobe significant.

The team member can use verbal commands to inform the playbook actiongenerator of the team member's state. The TSS 505 can include componentsand devices for team members to wear or carry that can provide sensorinformation to the system, as well as provide feedback from the systemto the team member. The information can indicate team member location,physiological state, and weapon status information. The components caninclude, but are not limited to, COTS products such as sensors, worn orcarried by the team members, that can provide weapon status information,location (e.g., via global positioning system (“GPS”)), a verbal commandinterface, and/or information on each team member's physiological state.GPS is global positioning satellite, a satellite navigation system thatallows accurate determination of a location. Any discussion of GPS isnot intended to be limited only to the global positioning satellite, butcan include any position locating or tracking system, including, forexample, global navigation satellite system (“GLONASS”) and Galileo.

There can be numerous embodiments for the TSS components. For example,the TSS 505 can be a modification and/or addition to a rifleman's suite,which disseminates information over a wireless network. Haptics canserve as soundless, non-intrusive alert devices. A GPS chipsetintegrated into a microcontroller box and interfacing with a teammember's personal role radio provides digital communications and teammember location reporting. A small box with an inertial sensor (e.g., anorienting device) mounted on the weapon can provide weapon pointinginformation. The soldier's weapon can also be instrumented with safety,trigger, and auxiliary switches, as well as a laser rangefinder ordesignator. A chest-strap or instrumented t-shirt can providephysiological responses to a tactical situation, fatigue estimation, andestimates of cognitive load. A laser pointer (e.g., a IZLID 1000P laserpointer) mounted on the weapon can be used as a pointing device toselect an object of interest (e.g., a possible IED). A non-intrusive“watch fob” display device can display status and imagery. It can becarried on the team member's belt and glanced at for situationalawareness and understanding. A minor change to a team member's weaponcan provide trigger-pull and safety status to the system. Solid stateaccelerometers at joints (e.g., back, thigh, ankle, knee, or hip) of theteam members can enable deduction of posture, pose, position, or gait. Ateam member can wear a vest or bodysuit that has strain gauges orsensors to detect heart rate, respiration, and perspiration. The teammember's canteen can also have a sensor to monitor the amount of wateror fluids consumed from the canteen. The soldier's weapon can beinstrumented for cooperative robotic weapon control. For example, asshown in FIG. 7, when a soldier 700 points and/or fires his weapon 710in a certain direction, a robotic platform 720 can point a weapon 730 ina substantially similar direction. The robot's weapon 730 can be asurrogate automatic or semi-automatic weapon that can be mounted on apan-tilt unit. Equipment for TSS may require the team member to carryextra weight, but eliminates bulky OCU equipment.

The TSS 505, 605 receives task results at an information manager 642,which transmits information to a verbal command module 643 and an alertmanager 644, which in turn communicates with alert devices 645. Theinformation manager 642 manages the network, captures messages, anddecides which messages to listen to and which messages to send. Forexample, the information manager 642 can decide whether to send amessage to a group or to one person and the best way of sending themessage. The information manager 642 also identifies the team member'sstate 646, such as geolocation, physiology, and weapon state. Theinformation manager 642 communicates the team data and tasks or commandsto the perceivers 611.

User feedback devices can enable alerts and information transfer back toteam members without distracting the team member or obstructing his orher senses. User feedback management software can provide user feedbackand manages the information flow to the team members, taking intoaccount available bandwidth, information criticality, the tacticalsituation, and the estimated cognitive burden.

Unmanned platform interface software 510, 610 can provide thefunctionality to provide connectivity from the C2 software 500, 600 toenable control of the unmanned vehicle. The unmanned vehicle interfacekits 510, 610 can have devices, software, and hardware for integratingthe C2 software 500, 600 with selected unmanned platforms. An unmannedvehicle common interface 510, 610 can use plug-in interfaces for thecapability of controlling current and future unmanned vehicle platforms.The unmanned vehicle interface 510, 610 has an unmanned vehicle nativeplatform controller 565, 665 that can control a common command interface570, 670, a common information interface 575, 675 and a common physicalinterface 580, 680. The native platform controller 565, 665 takesinformation from common command interface 570, 670 and the commoninformation interface 575, 675 and converts into the custom physicalinterface 580, 680. The common command interface 570, 670 translates thecommands from the plan execution 530, 630 in a common command languageinto a native controller language of the platform. The commoninformation interface 575, 675 collects information and providesproducts such as video, pictures, audio, and the like, to the feedbackmanager 535, 635. The custom physical interface 580, 680 can involve theinteraction with hardware or mounting, such as determining how to getpower or control pan tilt.

Referring to FIG. 8, an architecture demonstrating the interface betweenan unmanned vehicle framework 800 and an unmanned vehicle interface 810is shown. In the unmanned vehicle interface 810, which can resemble theunmanned vehicle interface 510, 610 in FIGS. 5 and 6, an unmannedvehicle native platform controller 820 can control a common commandinterface 825, a common information interface 830, and a custom physicalinterface 835. The common command interface 825 has modules such as aJAUS (Joint Architecture for Unmanned Systems) interface (e.g., toconvert a command a send it to the platform), teleoperation interfaces(e.g., determine how to teleoperate and convert for the platform),and/or any well-defined interface, whether or not it is a standard. Thecommon information interface 830 has modules such as a 2D (e.g., sendpictures from a soldier or sensor, video frames, or streaming media, ora laser line scanner), 3D (e.g., radar, ladar, laser range informationincluding distance), messages that go back and forth, and the like. Thecustom physical interface 835 has modules such as power, mechanicalconnections, network power, radio hookups, pan tilt, additional sensors,and/or other additional physical components. In one example of aninteraction between the components of the unmanned vehicle interface810, if the common command interface 825 and the common informationinterface 830 require a plug-in for a new device, the custom physicalinterface 835 may respond by querying whether a new antenna is needed.

The unmanned vehicle interface can convert the system information andcontrol data into native control directives for the base platform. Eachprotocol can be supported by a plug-in that handles the translation. Asa result, this approach can support new platforms and protocols.

The unmanned vehicle framework 800, which can represent software and/orhardware components shown in the TSS and C2 software shown in FIG. 5,can have tactical behaviors 840 (e.g., follow soldier or a verbalcommand) that are commanded to the common information interface 830. Theunmanned vehicle framework can also have a reasoning component 845,which receives information from the custom physical interface 835 toprovide to perceivers, such as a soldier perceiver or navigationperceiver.

The architecture in this exemplary configuration can be applicable toany unmanned vehicle or robotic platform. The architecture can bespecifically designed to utilize common interfaces that incorporateplatform-specific drivers. The software components interact with theseinterfaces (e.g., physical, command, and information) and the interfacesutilize platform-specific drivers to accomplish their tasks. Forexample, the software components may instruct the robotic system to “goforward 10 meters.” This command is passed to the command interface,which translates it to machine instructions via a driver for JAUS,CanBUS, USB, or other similar platform protocols. JAUS is the jointarchitecture for unmanned systems. JAUS is formerly known as jointarchitecture for unmanned ground systems (“JAUGS”). CanBUS is acontroller area network multicast-shared serial bus standard.

Referring to FIG. 9, an exemplary architecture is shown for the system.Mounted nodes 910, other information systems 915, and dismounted nodes905 can communicate with an information management component 920. Themounted nodes 910 can offer information regarding targeting, plans, anddetecting enemy soldiers. The other information systems 915 can be usedto monitor and analyze current on-the-ground command and control. Thedismounted nodes 905 can provide information from soldiers on abattlefield, including the platoon and company level, as well as thosesoldiers associated with a different unit. The information management920 can provide both situational awareness 935 as well as information tothe perceivers 930. Sensors 925 can also provide information to theperceivers 930. The sensors 925 can provide information to an annotated3D terrain map 965, which can be used by the perceivers 930 rather thanusing raw sensor data. The perceivers 930 and the situational awareness935 can be provided to the cognitive models 940, which also learn fromtraining 953, case based learning 963, and TTPs and mission plans 957.The cognitive models 940 include intent of a squad 945 and soldier 950,as described above with respect to the other architecturalconfigurations. The cognitive model 940 has a reasoner 955 that cangenerate a plan, which is sent to the unmanned vehicle control 960 forexecution. An executive 970 can direct the weapon control or vehiclecontrol 985, through tactical behaviors 975 and mobility behaviors 980.

Referring to FIG. 10, a method of autonomous control can proceed asfollows. First tactical behaviors can be developed 1010. Networkinformation can be used to convey information from team members and/orsensors 1020. A determination can be made as to the mission, situation,squad disposition, and soldier cognitive/emotional state 1030. Acognitive model can be driven 1040. The current state, goals, andintentions can be inferred 1050. An appropriate behavior can be selected1060. Soldier/robot TTP and tactical behavior can be mapped 1070.Optionally, feedback can be provided 1080. This method is not intendedto be limited to only these steps or the order thereof.

The system can enable the robotic platform or unmanned vehicle tounderstand the tactical situation by observing the team members. The TSScan provide the robot with location, weapon state, posture, andphysiology information. Each team member can serve as a sensor fordetecting the environment and interpreting it for the unmanned vehicle.The C2 software on each unmanned vehicle can collect information fromeach team member as well as from the robotic platform's sensors. Usingcognitive models, the C2 software reasons about how the team membershave positioned themselves, how they are moving, their postures, whetherthey are pointing their weapons, whether the weapons safeties are off,and the state implied by each team members physiology. In view of thisinformation, the system generates an estimate of squad intent, which isused to develop a short-term, simplistic plan known as the playbookaction (“PA”). The system executes the PA and monitors the results. Aslong as the PA remains valid, the unmanned vehicle continues to executeit. If the system changes the estimate of intent or receives directfeedback from a team member, the system can modify or replace thecurrent PA.

The PA generator derives a short-term play for the platform that isappropriate for the situation, understood and expected by the teammembers, and consistent with the team's training with the platform. ThePA generator evaluates the current tactical state provided by thecognitive model to determine which play to call from the playbook. Playsare short-term action plans, customized to fit the current situation.The playbook approach provides control at a high level of abstraction,but leaves the details of execution to the execution control andmonitoring layer of the system architecture. In the C2 paradigm, allteam members (human and robotic) share the same definition of a play(e.g., a battle drill) and understand the goals and acceptable behaviorsfor each member.

The playbook is developed based on current training materials and TTPs.The system selects a play on the fly by a team member's command overrideor by a situation and intent analysis. Playbooks minimize the necessityfor human interaction, while maximizing the capability of humans tointeract and control the situation for optimal achievement of missionobjectives.

Referring to FIG. 11 a, an exemplary playbook 1100 is shown. In thisplaybook 1100, the platform can choose between ammunition resupply,corpsman 911, logistics carrier, formation move, rally, designatorteleoperations, IED detection, and breach. In FIG. 11 b, once IEDdetection is chosen as the course of action, for example, the PAgenerator has short plays for the current tactical situation, including,but not limited to, detect designated object, discover designatedobject, calculate heading and distance, laser designation, net-centricdesignation, move to object, follow path, avoid obstacle, employ IEDsensor, deploy IED sensor, wait, and report results.

The execution control and monitoring layer sequences the PA, monitorsthe intermediate results, and determines if the play is succeeding,failing, or being overtaken by events. The play executive ensures thatthe PA created by the PA generator is executable and executed. The playsequencer is the primary executive for the platform. The play sequencerhas explicit knowledge about the system behaviors and the capabilitiesof the underlying platform. The play sequencer can be used to createplatform and context specific executable robotic actions that willachieve the objectives of the play. Real-time monitoring detectsexceptions in execution performance and exception handling providesrepair actions for exceptions identified by action monitoring.

The playbook monitor (“PM”) evaluates the status of the current PA,reasoning at the level of abstraction of the original play produced bythe PA generator to determine what feedback, if any, to provide to theteam member via the feedback manager.

In order to construct a computational cognitive model, the system canuse an existing cognitive framework, such as the Sandia CognitiveFramework, or build the cognitive model using languages such as ACT-R orSOAR.

The system can adapt, learn, and train with the team in an effort toavoid obsolescence or being overtaken by events. Learning can be basedupon many sources of information. Verbal commands, command overrides,and consent-by-taking-no-action provide feedback to the system on thequality of its understanding and action generation. TTPs, battle drills,x-files, and field manuals offer information on proper actions to takein tactical situations. Additionally, mission-recording andhuman-in-the-loop after action reviews can provide an environment inwhich situational understanding and action generation can be assessedand modified.

Training and learning can occur on many levels. Tactical preferences canbe minor modifications to the play. Team preferences for certain aspectsare not defined in TTP or battle drills. The system can learn to adjusttactical timing (e.g., the time interval between team members crossingthe street or a line of sight). The system understands roles, therebyoperating at a higher level of abstraction. When a team leader callsplays, a predefined PA is prompted by the PA generator. The cognitivemodel can learn how to respond to a new situation and how todifferentiate the new situation from the known situation (e.g., schemadifferencing). A team member guides a robot step-by-step through a newprocess, allowing the PA generator to build a new play. The new play canbe associated with a new verbal command, extending the commandvocabulary. Behavioral preferences are an extension of play recording.Data can be recorded during training and actual missions to provideadjustments to improve execution and coordination with a given team.

Learning can be test-based on confidence metrics derived from semanticnetwork situational understanding, case-based reasoning (e.g., comparingthe current situation with historical cases), learning from training(e.g., parameterization of “playbook” actions, when playbook actions areappropriate, and responsibilities of different squad roles), orreinforcement learning (e.g., feedback from soldiers when inappropriatebehavior is produced in the form of real-time verbal feedback orafter-action review). The system can also learn from soldier interactionor response to events and objects. The system can learn from asquad-specific approach to tactical situations or soldier-specificphysiological and behavioral response to threats. The system storescases to guide real-time assessment. The system can also collectconfirmatory information to validate a situational hypothesis.

Learning of tactics and appropriate action can be enabled whenever ahuman gives corrective input to the human. This can be in the form ofverbal override commands or “after action” analysis of the robot'sperformance. The human's corrective input can used to define anddifferentiate a situation where the new behavior is required and toenable the system to detect the appropriate situational markers (e.g.,team positioning, team actions, changes in human physiology) that can beused in the future to trigger the new behavior. Referring to FIG. 12,feedback from both the current robot's behavior and corrections from thehuman can initiate learning in the robot resulting in modification tothe state transition. A robot behavioral model 1210 can utilizeinferences from perception 1220, overt commands, or a human's actions tolearn a new behavior. The robot's actions and results are sent asfeedback to the human cognitive model 1200. Both the soldier and therobot can analyze the situation in view of the mission plan, tacticalpicture, and squad disposition.

For verbal commands, the system can include voice understanding, a fixedcommand set, command override, the ability to learn new verbal commands,and gesture commands with an instrumented glove. The command vocabularycan include, for example, point, flank, guard, and evac.

The systems and methods can combine long-wave infrared images from anIR-sensitive camera (e.g., a FLIR A20) with corresponding images fromother devices (e.g., two cameras in a stereo configuration, such as aPGR Bumblebee, a color camera, and a LADAR scanner) to detect humans.These sensors are integrated in the net-centric environment.

Each soldier or team member can be outfitted with a PDA, wearablecomputer, or a computer that is implemented in one of their devices,such as a computer in the scope of the weapon. The information regardingthe soldier can be transmitted through a wireless network from thecomputer to the unmanned vehicle or robotic platform.

Tactical maneuvers can include following a team member, rally to a namedpoint or team member, formation movement, maneuver to a fire position,wall hugging, low observability movement, and stealthy movement. Thesystem is also capable of tactical understanding and role-basedbehavior, including safe operations with instrumented team members orother personnel, simpler roles such as “guard in place,” or moresophisticated roles such as “point man” or “rear guard.”

In one example of this configuration, a team moves through a city withunmanned ground vehicles (e.g., a Talon) moving along with them toaugment the team's capabilities in remote inspection, improvisedexplosive device (“IED”) detection, and ammunition resupply. The teamsees an object and designates it as suspicious. The team verballycommands a Talon to inspect the suspicious object. The Talon employs IEDsensors and reports back to the team. As a result, a team member doesnot have to perform continuous operator control of the Talon and canmultitask while the Talon moves to and from the suspicious object. TheTalon is put in harm's way, rather than a human. Other benefits caninclude a reduction in time to achieve mission goals and capabilities ofthose team members due to an automation of repetitive tasks.Additionally, the unmanned ground vehicles assume risks from the teammembers, which can increase team member survivability.

In another example of this configuration, the team comes under fireduring a routine patrol. In this example, an unmanned ground vehicle(e.g., a Gladiator) is in point position and an unmanned aerial vehicle(e.g., a Dragon Eye) is automatically maintaining position a few blocksin front of the squad. When the team gets excited and point safety-offweapons at a location, the Dragon Eye sweeps back to give an overheadsituation awareness of the target area. The Gladiator moves towards thethreat, drawing fire and moving its sensors into a better position fordetection. The Gladiator monitors the street with its onboard sensorsuite, alerting the team to new intruders. In this example, the teammembers do not have to perform any operator control of the unmannedground/air vehicles or constantly monitor their progress. Additionally,these unmanned ground/air vehicles can augment the team's capabilitiesin scouting and reconnaissance without a team member on point, just incase an ambush occurs.

In another example of this configuration, the team is on a movement tocontact mission and aim their weapons at a threat. The team membersverbally command the Gladiator for cover fire. The Gladiator realizesthat the team members are stressed, their weapons are safety-off, andthey are firing at a threat. The Gladiator triangulates the threat'slocation and positions itself to cover fire on command. When a teammember is wounded, the Gladiator automatically provides cover for himand the corpsmen. On command from a corpsman, the Gladiator acts as aMEDEVAC when the wounded team member is stabilized. In this example, theunmanned ground vehicle can carry additional ammunition and medicalsupplies for a resupply, even under fire. The unmanned ground vehiclecan even provide additional suppressive fire to enable the team membersto maneuver.

In another example, the team members designate a vehicle and verballycommand the Talon to inspect it. The Talon autonomously approaches thevehicle and employs onboard sensors, reporting results back to the team.The Gladiator provides cover, ready to fire on command. The Gladiatormonitors team weapon status to determine threat status and respondsaccordingly. The Dragon Eye performs reconnaissance several miles ahead,alerting the team of approaching vehicles. In this example, the teammembers are provided with a greater standoff from potential threats andare provided with an early warning of approaching threats.

In one example of using this configuration, a soldier walks forward anda robot takes point in front of the soldier providing cover in case of asurprise attack. The soldier switches the safety to “off” on the weaponand assumes a tactical posture. The robot tacks back and forth in ageneral direction to flush out hidden enemies, maintain a view of thesoldier to analyze the body pose and hand signals, position itself toprovide cover for the crouching soldier, and retreat if necessary whenthe soldier starts retreating by providing rear guard and cover for thesoldier.

Key events and cooperative behaviors can include, but are not limitedto, team movement, formation change, verbal override, providing coverfire, ammunition resupply, and protecting a downed (e.g., injured orwounded) soldier. For team movement, the robot can move appropriatelyinto a formation, including line, wedge, or column. For formationchange, the robot can detect change in formation and respondaccordingly. For verbal override, the robot can change position fromdefault in response to verbal directive. For providing cover fire, therobot can detect soldier/team pose, elevated physiology, weaponorientation and status (e.g., safety “off” or firing), and estimateslocation of threat to provide cover fire for the team. For ammunitionresupply, the robot can detect soldier ammunition as low and move toresupply the soldier with additional ammunition. For protecting a downedsoldier, the robot can detect a wounded soldier through pose andphysiological status, maneuvering between the wounded soldier and theestimated threat.

In another example, referring to FIG. 13 a, a fire team ingresses anarea of building A using a satchel charge to enable an assault on anenemy force T. In FIGS. 13 b and 13 c, the fire team continues toingress the area and approaches using bounding overwatch. In FIG. 13 d,the soldiers begin firing on the target. In FIG. 13 e, robot R1 providescover fire. In FIG. 13 f, robot R2 runs gauntlet with the satchel chargeand drops it at a wall of building A. In FIG. 13 g, robot R2 movesoutside the explosive range and the soldiers detonate the charge. InFIG. 13 h, robots R1 and R2 provide cover fire as the soldiers storm thebreach.

In yet another example, referring to FIG. 14 a, a plurality of soldiersexecute a “through the door” TTP to breach a doorway and enter a roomhaving assaulting enemy forces therein. Referring to FIG. 14 b, soldier3 breaches the door. Referring to FIG. 14 c, soldier 3 retreats andsoldier 1 goes through the door and to the left. Referring to FIG. 14 d,soldier 2 goes through the door to the right. Referring to FIG. 14 e,soldier 3 goes through the door to the left. Referring to FIG. 14 f,soldier 4 does through the door to the right.

In an alternative configuration, robots can assume randomly selectedpositions or roles in the action. For example, referring to FIG. 14 g,robot R1 takes the place of soldier 3 and robot R2 takes the place ofsoldier 2.

In still yet another example, a soldier is wounded during an assault androbots work as a team to protect and evacuate the downed soldier.Referring to FIG. 15 a, a fire team is assaulting a defended enemyposition. Robots R1 and R2 provide rear security. Referring to FIG. 15b, soldier 2 is wounded. Soldier 2's impact sensor senses the wound andreports via the network. Referring to FIG. 15 c, robot R1 providesphysical cover and robot R2 provides wide-area cover fire. Referring toFIG. 15 d, soldier 4 assists to attach soldier 2 to robot R1. Referringto FIG. 15 e, robot R2 moves to provide physical cover. Referring toFIG. 15 f, soldier 4 moves back to cover, robot R1 drags soldier 2 outof the area, and robot R2 provides physical cover and cover fire.

When the system is utilized, a robot can perform cooperative, tacticallycorrect behavior without human interaction or cognitive burden. In adismounted mode, the robot operates as an integrated and trained memberof a team, understands team mission and tactics, needs no humanintervention during short-term high-intensity conflict, has situationalawareness and understanding by discovery and harvesting the net-centricinformation streams. In a mounted mode, a robotic “wingman” canautomatically support and protect a manned vehicle; can understandmachine readable mission plans, situational awareness and understanding,and targeting streams; can provide automated net-centric fire platform;and the automatic tactical behavior reduces the need for roboticcontrollers.

Referring to FIG. 10, in one configuration, the system is integrated ina small-unit unmanned ground vehicle for high-stress operations, such asmilitary operations on urban terrain (“MOUT”) scenarios, without usingan OCU. The system will use tactical behaviors; use netcentricinformation from instrumented sources to determine the mission,situation, squad disposition, and soldier cognitive/emotional state;monitor the soldiers' positions and poses to detect changes in atactical state; utilize a cognitive model of the soldier and the squadto infer the current state, goals, and intentions; based on theinference, select the appropriate behavior for the unmanned groundvehicle to support the squad in the current situation; map thesoldier/robot TTP and tactical behavior into the current situation andterrain; and provide non-computer, non-RF feedback to the soldier fromthe robot (e.g., pointing at a suspected enemy location). The system canbe constantly updated with information from external sources.

The embodiments described above are intended to be exemplary. Oneskilled in the art recognizes that numerous alternative components andembodiments that may be substituted for the particular examplesdescribed herein and still fall within the scope of the invention.

1. A system comprising: a plurality of sensors worn by a first teammember providing first team member information including at leastlocation, heart rate and blood pressure of the first team member; aplurality of sensors worn by a second team member providing second teammember information including at least location, heart rate and bloodpressure of the second team member; a plurality of sensors attached to aweapon of the first team member providing first weapon informationselected from the group consisting of ammunition supply, direction ofaim, safety status and trigger pull from the weapon of the first teammember; a plurality of sensors attached to a weapon of the second teammember providing second weapon information selected from the groupconsisting of ammunition supply, direction of aim, safety status andtrigger pull from the weapon of the second team member; a roboticplatform comprising: a team member perceiver for receiving the first andsecond team member information; a weapon perceiver for receiving thefirst and second weapon information; a team perceiver for receiving teamformation information; a reasoner in communication with the team memberperceiver, weapon perceiver and team perceiver for querying the teammember perceiver, weapon perceiver and team perceiver and forinterpreting the first and second team member information, the teamformation information, and the first and second weapon information inorder to estimate a team intent based on the first and second teammember information, the team formation information, and the first andsecond weapon information and determine a directive based on theestimated team intent; and a control unit in communication with thereasoner, for executing the directive.
 2. A method for controlling arobotic platform comprising: receiving, by a reasoner of the roboticplatform, first team member information including at least location,heart rate and blood pressure of a first team member from a team memberperceiver queried by the reasoner; receiving, by the reasoner, secondteam member information including at least location, heart rate andblood pressure of a second team member from the team member perceiverqueried by the reasoner; receiving, by the reasoner, first weaponinformation selected from the group consisting of ammunition supply,direction of aim, safety status and trigger pull from a weapon of thefirst team member from a weapon perceiver queried by the reasoner;receiving, by the reasoner, second weapon information selected from thegroup consisting of ammunition supply, direction of aim, safety statusand trigger pull from a weapon of the second team member from the weaponperceiver queried by the reasoner; determining, by the reasoner, teamformation information; estimating, by the reasoner, a team intent basedon the first and second team member information, the team formationinformation, and the first and second weapon information; determining,by the reasoner a directive based on the estimated team intent; andexecuting, by a control unit, the directive.